
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

lowerCAmelCase					:    Tuple           = [
    """cross_validation.py""",
    """gradient_accumulation.py""",
    """local_sgd.py""",
    """multi_process_metrics.py""",
    """memory.py""",
    """automatic_gradient_accumulation.py""",
    """fsdp_with_peak_mem_tracking.py""",
    """deepspeed_with_config_support.py""",
    """megatron_lm_gpt_pretraining.py""",
]


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_ = None						,				A_ = None     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     None
											UpperCamelCase   			=     os.path.abspath(os.path.join('examples'						,				'by_feature'     )     )
											UpperCamelCase   			=     os.path.abspath('examples'     )
											for item in os.listdir(_lowercase     ):
																		if item not in EXCLUDE_EXAMPLES:
																									UpperCamelCase   			=     os.path.join(_lowercase						,				_lowercase     )
																									if os.path.isfile(_lowercase     ) and ".py" in item_path:
																																with self.subTest(
																																    tested_script=_lowercase						,				feature_script=_lowercase						,				tested_section='main()' if parser_only else 'training_function()'						,				):
																																							UpperCamelCase   			=     compare_against_test(
																																							    os.path.join(_lowercase						,				_lowercase     )						,				_lowercase						,				_lowercase						,				_lowercase     )
																																							UpperCamelCase   			=     """\n""".join(_lowercase     )
																																							if special_strings is not None:
																																														for string in special_strings:
																																																					UpperCamelCase   			=     diff.replace(_lowercase						,				''     )
																																							self.assertEqual(_lowercase						,				''     )







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											self.one_complete_example('complete_nlp_example.py'						,				_lowercase     )
											self.one_complete_example('complete_nlp_example.py'						,				_lowercase     )







				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     os.path.abspath(os.path.join('examples'						,				'cv_example.py'     )     )
											UpperCamelCase   			=     [
											    """ """ * 16 + """{\n\n""",
											    """ """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
											    """ """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
											    """ """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
											    """ """ * 20 + """\"epoch\": epoch,\n\n""",
											    """ """ * 16 + """},\n\n""",
											    """ """ * 16 + """step=epoch,\n""",
											    """ """ * 12,
											    """ """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
											]
											self.one_complete_example('complete_cv_example.py'						,				_lowercase						,				_lowercase						,				_lowercase     )
											self.one_complete_example('complete_cv_example.py'						,				_lowercase						,				_lowercase						,				_lowercase     )


@mock.patch.dict(os.environ							,       {"""TESTING_MOCKED_DATALOADERS""": """1"""})
class 			SCREAMING_SNAKE_CASE__      (     a_):
				lowerCAmelCase_     			=							False







				@classmethod
				def 						UpperCAmelCase_    (       cls     )->		Any:







											'''simple docstring'''






											super().setUpClass()
											UpperCamelCase   			=     tempfile.mkdtemp()
											UpperCamelCase   			=     os.path.join(cls._tmpdir						,				'default_config.yml'     )

											write_basic_config(save_location=cls.configPath     )
											UpperCamelCase   			=     ["""accelerate""", """launch""", """--config_file""", cls.configPath]







				@classmethod
				def 						UpperCAmelCase_    (       cls     )->		List[Any]:







											'''simple docstring'''






											super().tearDownClass()
											shutil.rmtree(cls._tmpdir     )







				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     F'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        '''.split()
											run_command(self._launch_args + testargs     )
											self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,				'epoch_0'     )     )     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     F'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        '''.split()
											UpperCamelCase   			=     run_command(self._launch_args + testargs     )
											self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,				'step_2'     )     )     )







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     F'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir						,				"epoch_0"     )}
        '''.split()
											UpperCamelCase   			=     run_command(self._launch_args + testargs						,				return_stdout=_lowercase     )
											self.assertNotIn('epoch 0:'						,				_lowercase     )
											self.assertIn('epoch 1:'						,				_lowercase     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     F'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir						,				"step_2"     )}
        '''.split()
											UpperCamelCase   			=     run_command(self._launch_args + testargs						,				return_stdout=_lowercase     )
											if torch.cuda.is_available():
																		UpperCamelCase   			=     torch.cuda.device_count()
											else:
																		UpperCamelCase   			=     1
											if num_processes > 1:
																		self.assertNotIn('epoch 0:'						,				_lowercase     )
																		self.assertIn('epoch 1:'						,				_lowercase     )
											else:
																		self.assertIn('epoch 0:'						,				_lowercase     )
																		self.assertIn('epoch 1:'						,				_lowercase     )







				@slow
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     """
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
											with mock.patch.dict(os.environ						,				{'TESTING_MOCKED_DATALOADERS': '0'}     ):
																		UpperCamelCase   			=     run_command(self._launch_args + testargs						,				return_stdout=_lowercase     )
																		UpperCamelCase   			=     re.findall('({.+})'						,				_lowercase     )
																		UpperCamelCase   			=     [r for r in results if """accuracy""" in r][-1]
																		UpperCamelCase   			=     ast.literal_eval(_lowercase     )
																		self.assertGreaterEqual(results['accuracy']						,				0.75     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     ["""examples/by_feature/multi_process_metrics.py"""]
											run_command(self._launch_args + testargs     )







				@require_trackers
				@mock.patch.dict(os.environ						,				{'WANDB_MODE': 'offline'}     )
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											with tempfile.TemporaryDirectory() as tmpdir:
																		UpperCamelCase   			=     F'''
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            '''.split()
																		run_command(self._launch_args + testargs     )
																		self.assertTrue(os.path.exists(os.path.join(_lowercase						,				'tracking'     )     )     )







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     ["""examples/by_feature/gradient_accumulation.py"""]
											run_command(self._launch_args + testargs     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     ["""examples/by_feature/local_sgd.py"""]
											run_command(self._launch_args + testargs     )







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= [
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """encoder.embed_positions._float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_							,					a_						:						List[Any] 							= emb.weight.shape
			a_						:						Dict 							= nn.Linear(a__  ,	a__  ,	bias=a__)
			a_						:						str 							= emb.weight.data
			return lin_layer


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= torch.load(a__  ,	map_location="""cpu""")
			a_						:						int 							= mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
			a_						:						Dict 							= mam_aaa["""model"""]
			remove_ignore_keys_(a__)
			a_						:						List[str] 							= state_dict["""encoder.embed_tokens.weight"""].shape[0]

			a_						:						Union[str, Any] 							= MaMaaaConfig(
			    vocab_size=a__  ,	max_position_embeddings=1_0_2_4  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="""relu"""  ,	)

			a_						:						Union[str, Any] 							= state_dict["""decoder.embed_tokens.weight"""]
			a_						:						List[Any] 							= MaMaaaForConditionalGeneration(a__)
			model.model.load_state_dict(a__  ,	strict=a__)
			a_						:						Any 							= make_linear_from_emb(model.model.shared)

			return model


if __name__ == "__main__":
	__snake_case				:      Union[str, Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	__snake_case				:      int   		=			parser.parse_args()
	__snake_case				:      Any   		=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)


